Reservoir Labs (United States)

Abstract Objectives/Scope A comprehensive data collection and analysis plan was applied to a Permian Basin Devon Energy Bone Spring pad (the Devon Project) comprising 3 new wells and 2 recently drilled parent wells as shown in Figure 1. The goal of this ‘science pad’ was to characterize and quantify fracture growth between wells having stratigraphic and structural variation to further optimize well spacing, landing depths, and completion design. Methods, Procedures, Process To understand hydraulic fracture propagation and drainage behavior a suite of diagnostic tests were applied: Toe-stage conventional Diagnostic Fracture Injection Tests (DFITs) and repeat DFIT Flow-back (DFIT-FB) tests on the same toe ports,Post-frac stage fall-off analysis including a Pressure-Transient-Analysis (PTA) technique to estimate treating well perforation cluster efficiency,Fracture Driven Interaction (FDI) statistics and modeling including Sealed Wellbore Pressure Monitoring (SWPM),Post completion sequential flow-back interference testing,Parent well production diagnostics. Results, Observations, Conclusions Repeat testing of the toe-port with conventional DFITs and DFIT-FB presented a unique opportunity to compare results using different testing and analysis techniques in exactly the same location. DFIT execution was again shown to be critical to achieve the best results. DFIT-FB interpretation was challenging in this setting with low net pressure, Pnet, and net stress, Ps (defined in the nomenclature section). PTA diagnostics and modeling workflows were applied to select frac stages having sufficient post-frac stage fall-off data. This analysis provides greater clarity and quantification of well friction, perforation friction, near-well tortuosity, and Far-field Fracture Extension Pressure (FFEP). Results from the suite of diagnostic tests applied on this pad presented an important example of ‘the whole is greater than the sum of the parts’. Individual tests results, taken alone, may have been interpreted as misruns, inconclusive, or contradictory. However, taken in concert, results provided a clear picture of ‘unconventional’ fracture growth and likely depletion gaps that will be used by Devon for further testing of well spacing and fracture designs to optimize development. Novel/Additive Information Pressure data acquisition is known to be inexpensive and non-disruptive in most completion operations. However, this project demonstrated the value of more data and repeat testing to help define the range of outcomes and to distinguish when data may be indicating representative vs outlier behavior. Pre-planning and investment in higher resolution pressure gauges located on all wells-of-interest to capture uninterrupted pressure measurements is recommended.

Abstract Background Following the impoundment of the Three Gorges Reservoir, deposit-layer landslides in the reservoir basin have experienced cyclic deformation under the combined influence of seasonal precipitation and periodic water-level operations. The deformation amplitude accumulates annually, continuously bringing uncertainty to the safety of the reservoir basin. Purpose This study takes the Tanjiahe landslide (a typical feature in the Three Gorges Reservoir region) as the research object. By integrating its recent daily displacement measurements, daily rainfall data, and reservoir water-level observations, this research aims to establish a deep learning-based prediction model to forecast the landslide’s long-term deformation trends. Specifically, the aggregated displacement time series is segmented into a trend term and a cyclic term, so as to separately characterize the landslide’s long-term evolution and periodic dynamic behavior. Methods First, Grey Relational Analysis (GRA) and Pearson’s correlation analysis are used to identify the dominant influencing factors of the Tanjiahe landslide (including antecedent rainfall, reservoir water-level variation characteristics, and past displacement increments) as input features. Then, considering the timeseries nature of monitoring data and limited sample size constraints, a Long Short-Term Memory (LSTM) network is developed to predict the landslide’s cyclic displacement, while the trend component is modeled via Support Vector Machine (SVM) regression. Results The results indicate that the LSTM method significantly outperforms in depicting the complex nonlinear time-dependent features of landslide deformation; the SVM method stably captures the aggregate deformation trajectory and shows strong performance in low-frequency trend regression. Additionally, Rescaled Range (R/S) analysis of observed versus predicted sequences reveals significant long-term persistence in the Tanjiahe landslide’s deformation evolution at its current phase. Conclusion This study demonstrates that the combined LSTM-SVM model can effectively predict the Tanjiahe landslide’s long-term deformation. The identified long-term persistence of deformation provides a basis for long-term risk assessment and early warning of this landslide.

Abstract The subsurface data acquisition during drilling, mud logging, coring, core analysis, rock mechanical tests, and well logging was meticulously executed to identify the optimal salt interval for cavern hosting. This involved comprehensive salt composition analysis, potential salt interval identification, lateral salt extent analysis, fracture and fault network analysis, in situ stress and salt stability analysis, and porosity and permeability assessment. This thorough approach ensured precise characterization of salt, paving the way for optimal cavern development. A comprehensive subsurface data acquisition was conducted from drilling, mud logging, coring, and advanced geophysical well logging. Techniques like XRD, XRF, DE CT, elemental logs, and spectral gamma were used to analyze salt composition. Core description and multivariant logs identified potential salt and insoluble intervals. 3D borehole electromagnetic logs and VSP tested lateral salt extent. Fracture and fault networks were assessed using core CT scans, image logs, and seismic data. In situ stress and salt stability were analyzed through mini-frac, rock mechanical tests, creep test, dissolution and thermal expansion tests. This thorough data acquisition ensured accurate characterization for cavern design. The study provided significant insights by integrating multidisciplinary data and utilizing a robust approach to derisk subsurface parameters. Salt and insoluble facies were characterized by integrating core measurements such as sedimentology, dual energy CT, XRD, XRF, thin section, and SEM studies, and correlating them with various logs. Salt facies classification considered factors like salt type and percentage, crystal size and shape, color, impurities, and bedding effects, while insoluble facies were based on lithologies, size, color, distribution, and porosity types. A wide variety of wireline logs, both basic and advanced, were used for multimineral analysis, calibrated with core descriptions, XRD, and XRF, to ensure accurate determination of salt and insoluble content, porosity, and permeability. New technologies like 3D borehole electromagnetic logs and VSP confirmed the vertical and lateral extent of salt. Stress and mechanical behavior were assessed through mini-frac tests, core rock mechanical tests, and dipole shear imager. Various mechanical tests, including unconfined compressive tests, triaxial tests, Brazilian tests, long-term creep tests, dissolution tests, and thermal expansion tests, were conducted to ensure salt stability. Integration of all data qualified salt intervals for leaching design and operation, developing the desired underground storage volume.

Abstract Seismic imaging in the Tarim Basin, China, presents significant challenges due to complex near-surface conditions, including desert environments, foothills, and loess mountains. These challenges are comparable to those found in regions such as the Arabian Peninsula, North Africa, the Andes Mountains, and other complex terrains worldwide. Recent advancements in seismic imaging aimed at overcoming these obstacles include: (1) advanced constrained near-surface tomography, which has significantly enhanced the robustness of near-surface velocity-depth model estimation, leading to improved resolution of deep reservoir images; (2) a recent ultra-long-offset (greater than 15 km) experiment in the foreland basin of the Tarim Oilfield, demonstrating that more accurate and deeper near-surface velocity models can be generated on land using turning-ray tomography; (3) finite-frequency wavepath tomography, which has been proven through field data examples to be a robust alternative to traditional full-waveform inversion for land seismic data; and (4) integrated tomography, which combines diving waves and reflected seismic data to develop a comprehensive velocity model from shallow to deep sections for anisotropic prestack depth migration from topography. These technologies, developed for and applied to the Tarim Basin, can be applied, with modifications, to other land basins around the world.

ABSTRACT It is a common yet perplexing phenomenon in the oil industry that high-saturation zones, as indicated by petrophysical measurements, are frequently perforated and tested, yet they often yield negligible or no oil flow, which can be quite puzzling and disappointing. Additionally, the original oil in place (OOIP) is typically estimated through conventional core analysis techniques and geochemical tests, such as programmed non-isothermal pyrolysis. However, these estimates are often revised over the lifecycle of the field as the actual OOIP values are found to deviate from the predicted production profile. A relatively new method, low-temperature hydrous pyrolysis (LTHP), was introduced by Lewan and Sonnenfeld (2017) to improve reserve estimates and production forecasts via more accurate quantification of mobile oil saturation. As they described for mature Niobrara cores, LTHP releases retained oil that is similar to produced oil. LTHP is a technique in which crushed mature source-rock core is isothermally heated in contact with liquid water at 300°C for 12 to 24 hours. These conditions, allow retained oil to be released from the mature rock by thermal expansion and water displacement of the oil and its dramatic difference in miscibility with polar-rich bitumen in the presence of water, but are not sufficient to generate oil from the bitumen or kerogen within mature source rocks. The method has also been extended to determine quality and quantities of retained oil in non-source rock tight-oil reservoirs. The quantities of released oil typically are sufficient to determine mobile oil, API gravity, gas chromatography, and gas chromatography/mass spectrometry signatures. The examined well exhibits signs of substantial hydrocarbon saturations in primarily carbonate lithology reservoir sections, as determined by log evaluations. Routine core analyses show hydrocarbon saturations that appear to be tar/bitumen but with a potentially mobile oil component as indicated by elevated S1 yields from programmed pyrolysis results. Subsequent stimulation of these reservoir intervals resulted in low oil recovery despite the indications of good hydrocarbon saturation. Consequently, the LTHP workflow was implemented on core samples to quantify (1) "vaporizable oil", (2) "mobile/immobile oil", (3) "total oil" and (4) "Enhanced Oil Recovery (EOR) oil". Additionally, the High-Resolution Gas Chromatography (HRGC) fingerprints of the LTHP oils from these core samples were evaluated for standard geochemical parameters to evaluate the probable characteristics of the mobile oil and assess any secondary alteration via processes like water washing and/or biodegradation. The results show that mobile oil represents only a minor fraction (28% in zones of high saturation) of the in-situ total oil and that vaporizable oil significantly overestimates the mobile oil fraction likely due to vaporization of organic bound immobile oil associated with tar/bitumen. Zones of elevated total and mobile oil appear to be in intervals free of dolomite. HRGC fingerprints of mobile oil show loss of volatile compounds but retain intact n-alkane envelopes and molecular ratios that are consistent with a peak to late oil window thermal maturity and a likely marine carbonate or marine shale source. LTHP, which releases a true and collectable petroleum phase, is not intended to replace conventional core analyses or programmed pyrolysis, but rather supplement these better known, inexpensive and more rapid analyses.

Anxiety is a psychological and physiological state characterized by somatic, emotional, cognitive and behavioural components. It is the displeasing feeling of fear and concern. Every person experiences some form of anxiety throughout life. Anxiety can provide the stimulus for changes and growth and even depression, some anxiety is positive and even necessary. However too much anxiety can result in poor judgment, physical illness and inability to cope. A study indicates that health care providers and especially nurses, can have a major role in reducing parental distress by maintaining ongoing communication with parents and providing competent care for their infants. Since this study was carried out on a small convenience sample, the result can be used only as a guide for further studies.

Abstract This paper investigates asphaltene deposition in an oil well produces from a deep Jurassic reservoir located in Kuwait by developing a three-phase asphaltene deposition integrated model. The objective of this study is to develop and validate an integrated well model that incorporates asphaltene thermodynamic, aggregation, deposition, and transportation models coupled with a three-phase thermal-hydrodynamic mechanistic model. The proposed model is numerically solved to predict temporal and spatial asphaltene deposition and concurrently tracks oil composition changes due to asphaltene deposition. The elemental models of the integrated model are tuned against field data, and the deposition result is validated with a caliper log data. The field validation study revealed a very good match between the measured deposition behavior and model predictions. In addition, sensitivity analysis showed that although asphaltene precipitation increases as water cut increases due to higher pressure drop, asphaltene deposition decreases due to increasing shear stress and decreasing asphaltene sticking tendency on the pipe wall. Furthermore, sensitivity analysis revealed that as GOR increases, asphaltene precipitation increases; however, asphaltene deposition decreases. The proposed model shows significant sensitivity to asphaltene particle diameter, revealing that as particle diameter increases, deposition thickness decreases.